Min–max mean squared error-based linear transceiver design for multiple-input–multiple-output interference relay channel

2015 ◽  
Vol 9 (6) ◽  
pp. 853-861 ◽  
Author(s):  
Vindheshwari P. Singh ◽  
Ajit Kumar Chaturvedi
Keyword(s):  
Mean Squared Error ◽  
Multiple Input Multiple Output ◽  
Relay Channel ◽  
Output Interference ◽  
Transceiver Design ◽  
Squared Error ◽  
Multiple Input ◽  
Input Multiple Output

scholarly journals MIMO Detectors Under Correlated Channels

2016 ◽  
Vol 37 (1) ◽  
pp. 3
Author(s):  
Bruno Felipe Costa ◽  
Alex Miyamoto Mussi ◽  
Taufik Abrão
Keyword(s):  
Maximum Likelihood ◽  
Interference Cancellation ◽  
Mean Squared Error ◽  
Multiple Input Multiple Output ◽  
Successive Interference Cancellation ◽  
Correlated Channels ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Multiple Input ◽  
Input Multiple Output

Este artigo analisa o desempenho de detectores com múltiplas antenas transmissoras e múltiplas antenas receptoras (MIMO – multiple-input multiple-output) em canais com desvanecimento correlacionados. Dois esquemas de detecção MIMO denominados erro quadrático médio mínimo (MMSE – minimum mean squared error) – com ou sem a etapa de cancelamento de interferência sucessiva ordenado (OSIC – ordered successive interference cancellation) – e técnica de redução treliça (LR – lattice reduction) são analisados e comparados com o limite de detecção de máxima verossimilhança (ML – maximum likelihood) em cenários específicos de interesse: (a) com incremento da eficiência espectral através do aumento do número de antenas. (b) quando há aumento nos índices de correlação de desvanecimento do canal. Neste contexto, o desempenho do detector ótimo ML-MIMO é utilizado como referência visando caracterizar o comportamento da taxa de erro de bit (BER) destes detectores MIMO e quão próximo esses estão do desempenho ML-MIMO.

scholarly journals Gaussian MIMO relay channel with orthogonal channel components

2020 ◽  
Vol 71 (3) ◽  
pp. 222-226
Author(s):  
Zouhair Al-qudah
Keyword(s):  
Channel Model ◽  
Multiple Input Multiple Output ◽  
Relay Channel ◽  
Numerical Examples ◽  
Mimo Relay ◽  
Multiple Input ◽  
Communication Devices ◽  
Input Multiple Output ◽  
Theoretical Results

AbstractThe Gaussian multiple-input multiple-output (MIMO) orthogonal relay channel (ORC) is investigated. The transmission from source to relay is done over a channel that is orthogonal to source-destination and relay-destination channels. Practically, this assumption is made such that many communication devices from different technologies are exploited in relaying the source’s signal into its destination. For this channel model, the capacity is initially derived. Thereafter, we propose a transmission algorithm to achieve the derived capacity. Further, to support our theoretical results, many numerical examples are presented.

scholarly journals Joint Optimization of Precoder and Decoder in Multiuser MIMO Systems

2012 ◽  
Vol 2 (1-2) ◽  
Author(s):  
M. Raja ◽  
Ha H. Nguyen ◽  
P. Muthuchidambaranathan
Keyword(s):  
Mean Squared Error ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Joint Optimization ◽  
Phase Shift Keying ◽  
Multiuser Mimo ◽  
Multiple Input ◽  
Shift Keying ◽  
Input Multiple Output ◽  
Binary Phase Shift Keying

This paper considers the joint optimization of precoder and decoder for both uplink and downlink transmissions in multiuser multiple-input, multiple-output (MU-MIMO) systems. Focusing on the scenario when an improper constellation such as binary phase shift-keying (BPSK) or M-ary amplitude shift-keying (M-ASK) is employed, novel joint linear precoders and decoders are proposed to minimize the total mean squared error (TMSE) of the symbol estimation. The superiority of the proposed transceivers over the previously-proposed designs is thoroughly verified by simulation results.

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